The EEG signals may have temporal dependencies spanning multiple ranges. Graph convolution layers can successfully model spatial dependencies but fall short of extracting time-varying patterns in long sequences. 1-D convolutional layers can be helpful in extracting temporal patterns. However, they are limited by their receptive field which grows in a linear progression with the depth of the network, requiring a deeper network that is harder to converge during optimization due to vanishing gradients. On the other hand, dilated convolutions [1]} provide exponentially expanding receptive fields without losing resolution. Hence, the dilated convolution layers are a good choice for extracting temporal patterns of the input time series data in our model. However, choosing the right filter size is a difficult task as the temporal dependencies arising due to reading task difficulty have large variations. A very large filter size may miss critical recurring patterns while a shorter than required size may overfit the model.
\(\scalebox {.9}{\mbox{$\displaystyle \bar{x}_d = x \otimes f_{1 \times d} (t)= \int _{-\infty }^{\infty }f_{1 \times d}(s)x(t-r*s)$}}\) 
